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No one changes the world alone. AI models are rapidly learning to reason better, code faster, and take actions in the world with increasing autonomy. But for humans, progress happens when we understand one another, build trust, make connections, and work together. That is where we believe the next chapter of AI should begin.

Today we introduce humans&, a human-centric frontier AI lab. We believe AI can be reimagined, centering around people and their relationships with each other. At its best, AI should serve as a deeper connective tissue that strengthens organizations and communities.

January 20, 2026

This requires rethinking everything about how we train models at scale and how people interact with AI. This needs innovations in long-horizon and multi-agent reinforcement learning, memory, and user understanding. At humans&, we will tightly integrate science and product development to drive this new paradigm.

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We are builders and researchers who have shaped modern AI: people who have built and scaled seminal work in reasoning, behavioral training, agents, and alignment. Our founding team has led major efforts across frontier industry and academic labs including xAI, Anthropic, Google DeepMind, OpenAI, Meta, Reflection, AI2, Stanford, and MIT. We have collectively shipped models and products loved by billions of people.

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We are grateful for supporters invested in our vision: our seed round is led by SV Angel & co-founder Georges Harik. Our investors include NVIDIA & Jeff Bezos & GV (Google Ventures) & Emerson Collective & Forerunner & S32 & DCVC & Human Capital & Liquid 2 & Felicis & CRV & many others.

Including Exoscaleton (w/ Acrew), AME Cloud Ventures (Jerry Yang), Palo Alto Growth Capital, Conviction, Bloomberg Beta, E14, A&E Investment, Zeta Holdings, and individuals including Eric Zelikman (co-founder), Anne Wojcicki, Ralph Harik, Sarah Liang, Bill Maris, Marissa Mayer, James Hong, Stephen Balaban, Ying Sheng, David Wallerstein, Thomas Wolf, Mitesh Agrawal, Nikola Petrov Borisov, Yuhuai (Tony) Wu, Igor Babuschkin, Itamar Arel, Sharon Zhou, Thomas Reardon, Zak Stone, Logan Kilpatrick

&you

If you've done world-class work and want to shape the future of human-centered AI, join us here. We intend to contribute back to open source and academic research - if you'd love to collaborate, follow us @humansand and stay tuned.

Cultural Dynamics

This simulation visualizes cultural dissemination, inspired by the Axelrod model (see Axelrod, 1997). Each particle represents an agent with discrete cultural traits. Nearby agents interact and may adopt each other's traits based on their similarity. With social repulsion enabled, dissimilar agents actively differentiate themselves, creating cultural polarization (see Radillo-Diaz, et al., 2009). With cultural noise enabled, agents may spontaneously change their traits, creating cultural diversity (see Klemm, et al., 2003). The colors reflect the agents' evolving cultural identities as they shift through the day/night cycle.

Simulation Parameters

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